Due to the increasingly fast processing power of modem-day computers, users have turned to computers to assist them in the examination and analysis of images of real-world data. For example, within the medical community, medical professionals who once examined x-rays hung on a light screen now use computers to examine images obtained via ultrasound, computed tomography (CT), magnetic resonance (MR), ultrasonography, positron emission tomography (PET), single photon emission computed tomography (SPECT), magnetic source imaging, and other imaging techniques.
Each of the above-identified imaging procedures generates volume images, although each relies on a different technology to do so. For example, CT uses an x-ray source to rapidly rotate around a patient to obtain up to hundreds of electronically stored pictures of the patient. On the other hand, MR uses radio-frequency waves that are emitted to cause hydrogen atoms in the body's water to move and release energy, which is then detected and translated into an image. Because each of these techniques penetrates the body of a patient to obtain data, and because the body is three-dimensional, this data represents a three-dimensional image, or volume. In particular, CT and MR both provide three-dimensional (3D) “slices” of the body, which can later be electronically reassembled.
An important aspect in the use of medical images for diagnosing physiological conditions is the accurate segmentation of the region of interest in the image to identify the boundaries of the region and other anatomical structures. For example, assignment of the appropriate therapy or dose to treat a physiological condition may necessitate the volume being accurately measured. In 3D medical images, both boundary-based segmentation methods and region-based segmentation methods may be used to assist in the segmentation of organs, pathologies, vessel contours, or other volumes of interest. In boundary-based segmentation methods, the boundaries of the volume of interest are outlined by the user with an on-screen tool. In region-based segmentation methods, the user establishes seed points as examples of the regions to include in or exclude from the segmented image. However, in both methods, the user has a limited visibility of the volume of interest to segment or to edit, and the process of defining the boundaries of the volume of interest in three dimensions can be a very time consuming process.